{"id":309,"date":"2012-11-03T20:32:15","date_gmt":"2012-11-03T20:32:15","guid":{"rendered":"http:\/\/ahay.org\/blog\/?p=309"},"modified":"2015-09-02T15:31:39","modified_gmt":"2015-09-02T15:31:39","slug":"program-of-the-month-sfbandpass","status":"publish","type":"post","link":"https:\/\/ahay.org\/blog\/2012\/11\/03\/program-of-the-month-sfbandpass\/","title":{"rendered":"Program of the month: sfbandpass"},"content":{"rendered":"

sfbandpass<\/a> implements bandpass filtering using the Butterworth algorithm. <\/p>\n

The desired bandwidth is specified by low and high frequencies (in Hertz) flo=<\/strong> and fhi=<\/strong>. A continuous low-pass Butterworth filter is given by<\/a>
\n$$B_N^2(\\omega) = \\displaystyle \\frac{1}{\\displaystyle 1+\\left(\\frac{\\omega}{\\omega_0}\\right)^{2N}}$$
\nThe actual filtering is implemented by recursive (Infinite Impulse Response) convolution in the time domain. The number of poles ($N$) for the low-pass and high-pass filters are specified by nplo=<\/strong> and nphi=<\/strong> parameters. <\/p>\n

The following example from sep\/pwd\/hector<\/a> shows a land shot gather after linear moveout and low-pass filtering that isolates ground-roll noise.<\/p>\n

\"\" <\/p>\n

By default, the applied filter has zero phase. To use a minimum-phase filter, use phase=y<\/strong>. This is the option used in the Madagascar test example from rsf\/rsf\/test<\/a><\/p>\n

\"\" <\/p>\n

An alternative way to implement bandpass filtering is using the Fourier transform and tapering functions in the Fourier domain. One example of this approach is provided by sferf<\/a>. <\/p>\n

10 previous programs of the month<\/h3>\n